Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection.
Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection.
复制标题
外部球粒神经元的特性可以支持最佳动作选择。
DOI：
10.1371/journal.pcbi.1005004
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发表时间：
2016-07
影响因子：
4.3
通讯作者：
Baufreton J
中科院分区：
文献类型：
--
作者：
Bogacz R;Martin Moraud E;Abdi A;Magill PJ;Baufreton J
外苍白球（GPe）是基底神经节回路中的一个关键核团，被认为参与了动作选择。一类计算模型假设，在动作选择过程中，基底神经节计算在给定上下文中所有可用的动作的概率，这些动作应该被选择。这些模型表明，GPe和丘脑底核（subthalamic nucleus，简称ENA）神经元的网络计算贝叶斯方程中的归一化项。为了执行这样的计算，GPe需要将反馈发送到神经元，该反馈等于神经元活动的特定函数。然而，这种功能的复杂形式使得它不太可能，个别GPe神经元，甚至一个单一的GPe细胞类型，可以计算it. In这里，我们演示了如何计算这个功能可以在一个网络中包含两种类型的GABA能GPe投射神经元，所谓的“原型”和“arkypallidal”神经元，有不同的响应特性在体内和不同的连接。我们将我们的模型预测与实验报告的GPe神经元的两个群体的连接和输入-输出函数（f-I曲线）进行比较。我们表明，在一起，这些二分细胞类型满足必要的要求，计算所需的最佳行动选择的功能。我们的结论是，凭借其独特的响应特性和连通性，arkypallidal和原型GPe神经元的网络包括一个神经基板能够支持计算的后验概率的行动。在特定情况下尽可能快速、准确地选择适当的行动对于动物和人类的生存至关重要。参与动作选择的大脑区域之一是一组被称为基底神经节的皮质下核团。了解基底神经节中信息处理的重要性进一步强调了它们在帕金森病中的干扰相互作用导致运动的深刻困难。计算模型已经提出了基底神经节如何以最快的方式选择动作，以达到所需的准确度。这些模型进一步预测，基底神经节的一部分，称为外部苍白球（GPe），需要计算其输入的特定函数。本文提出了如何在GPe内的网络的数学模型中计算该函数。此外，它表明，实验观察到的GPe神经元的连接和响应特性满足必要的要求，以支持最佳的动作选择。这表明GPe神经元具有允许它们在整个基底神经节中有助于最佳动作选择的特性。
The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes’ equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called ‘prototypic’ and ‘arkypallidal’ neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions. Choosing an appropriate action as quickly and accurately as possible in a given situation is critical for the survival of animals and humans. One of the brain regions involved in action selection is a set of subcortical nuclei known as the basal ganglia. The importance of understanding information processing in the basal ganglia is further emphasised by the fact that their disturbed interactions in Parkinson’s disease results in profound difficulties in movement. Computational models have suggested how the basal ganglia could select actions in the fastest possible way for the required accuracy level. These models further predict that a part of basal ganglia, called the external globus pallidus (GPe), needs to calculate a particular function of its inputs. This paper proposes how this function could be computed in a mathematical model of a network within GPe. Furthermore, it shows that the experimentally observed connectivity and response properties of GPe neurons fulfil the requirements necessary to support optimal action selection. This suggests the GPe neurons have properties that allow them to contribute to optimal action selection in the whole basal ganglia.